• Journal of the Korean Statistical Society
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• 제25권2호
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• pp.235-241
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• 1996

The ordinary least squares estimator of the disturbance variance in the linear regression model with stationary errors is shown to be asymptotically unbiased when the error process has a spectral density bounded from the above and away from zero. Such error processes cover a broad class of stationary processes, including ARMA processes.

• 한국수학교육학회지시리즈A:수학교육
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• 제22권3호
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• pp.43-47
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• 1984

• 한국경영과학회지
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• 제21권1호
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• pp.163-170
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• 1996

An errors-in-variables model (EVM) differs from the classical regression model in that in the former the independent variable is also subject to error. This paper shows that to assess the applicability of the ordinary least squares (OLS) estimation procedure to the EVM, the relative dispersion of the independent variable to its error variance must be also considered in addition to Mandel's criterion. The effect of physically reducing the variance of errors in the independent variable on the performance of the OLS slope estimator is also discussed.

• ETRI Journal
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• 제26권5호
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• pp.413-422
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• 2004

This paper investigates the impacts of array weight errors (AWE) in an antenna array (AA) on a parallel interference cancellation (PIC) receiver in uplink synchronous and asynchronous direct sequence code division multiple access (DS-CDMA) systems. The performance degradation due to an AWE, which is approximated by a Gaussian distributed random variable, is estimated as a function of the variance of the AWE. Theoretical analysis, confirmed by simulation, demonstrates the tradeoffs encountered between system parameters such as the number of antennas and the variance of the AWE in terms of the achievable average bit error rate and the user capacity. Numerical results show that the performance of the PIC with the AA in the DS-CDMA uplink is sensitive to the AWE. However, either a larger number of antennas or uplink synchronous transmissions have the potential of reducing the overall sensitivity, and thus improving its performance.

• 한국정밀공학회지
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• 제16권3호통권96호
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• pp.109-115
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• 1999

This paper presents a stabilization control designed to improve position stabilization performance of a position servo-system(turret) mounted on a manuvering platform(vehicle). In the consideration of the motion of the platform, a dynamic model of the stabilization system is derived and shows the viscous and stick-slip friction torques are the major source of stabilization errors. An extended generalized minimum variance control which consists of a feedforward disturbance compensation as well as a pole placement feedback control is suggested to reduce the stabilization errors caused from the friction disturbances. This modeling and control are applied to a small experimental set-up and the experimental results confirm the accuracy of the model and the effectiveness of the suggested control.

• International Journal of Internet, Broadcasting and Communication
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• 제11권1호
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• pp.47-53
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• 2019

The idea of ensemble learning is to train multiple models, each with the objective to predict or classify a set of results. Most of the errors from a model's learning are from three main factors: variance, noise, and bias. By using ensemble methods, we're able to increase the stability of the final model and reduce the errors mentioned previously. By combining many models, we're able to reduce the variance, even when they are individually not great. In this paper we propose an ensemble model and applied it to classification problem. In iris, Pima indian diabeit and semiconductor fault detection problem, proposed model classifies well compared to traditional single classifier that is logistic regression, SVM and random forest.

• 대기
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• 제17권1호
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• pp.27-44
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• 2007

The purpose of this study is to investigate predictability of the seasonal simulation by the METRI (Meteorological Research Institute) AGCM (Atmospheric General Circulation Model), which is a long-term prediction model for the METRI 3-month prediction system. We examine the performance skill of climate simulation and predictability by the analysis of variance of the METRI AGCM, focusing on the precipitation, 850 hPa temperature, and 500 hPa geopotential height. According to the result, the METRI AGCM shows systematic errors with seasonal march, and represents large errors over the equatorial region, compared to the observation. Also, the response of the METRI AGCM by the variation of the sea surface temperature is obvious for the wintertime and springtime. However, the METRI AGCM does not show the significant ENSO-related signal in autumn. In case of prediction over the east Asian region, errors between the prediction results and the observation are not quite large with the lead-time. However, in the predictability assessment using the analysis of variance method, longer lead-time makes the prediction better, and the predictability becomes better in the springtime.

• 산업경영시스템학회지
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• 제35권3호
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• pp.52-61
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• 2012

The design method for cumulative sum (CUSUM) control charts, which can be robust to autoregressive moving average (ARMA) modeling errors, has not been frequently proposed so far. This is because the CUSUM statistic involves a maximum function, which is intractable in mathematical derivations, and thus any modification on the statistic can not be favorably made. We propose residual-based robust CUSUM control charts for monitoring autocorrelated processes. In order to incorporate the effects of ARMA modeling errors into the design method, we modify parameters (reference value and decision interval) of CUSUM control charts using the approximate expected variance of residuals generated in model uncertainty, rather than directly modify the form of the CUSUM statistic. The expected variance of residuals is derived using a second-order Taylor approximation and the general form is represented using the order of ARMA models with the sample size for ARMA modeling. Based on the Monte carlo simulation, we demonstrate that the proposed method can be effectively used for statistical process control (SPC) charts, which are robust to ARMA modeling errors.

• 한국정보통신학회논문지
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• 제12권6호
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• pp.1045-1050
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• 2008

위성항법보강시스템은 위성항법시스템 (GNSS : Global Navigation Satellite System)의 측정값에 오차 성분을 제거하여 정밀한 항법 정보를 제공한다. 하지만 기준국과 이동국 사이의 공통오차(전리층 지연, 대류층지연, 위성 시계오차, 궤도력 오차)는 제거할 수 있지만 전리층, 대류층, 신호잡음 등에 의해 발생하는 노이즈 성분의 비공통 오차는 제거 할 수 없다. 비공통오차는 항법정보의 오차를 발생하여, 항공기가 잘못된 위치 정보를 사용할 수 있으며, 특히 이 착륙시에 잘못된 항법정보는 항공기 안전에 심각한 영향을 미치게 된다. 본 논문에서는 다중 기준국을 사용하여 유동적인 전리층 및 대류층 습윤 증기량에 따라 비공통오차의 증가를 위치 오차 분산 변화를 통해 확인하였다.

• 한국측량학회지
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• 제35권6호
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• pp.573-580
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• 2017

GPS 관측값을 이용하여 측위를 하는 경우 조정계산이 수반되면 이때 관측오차에 대한 정확한 모델링이 필수적이다. 본 연구에서는 GPS 관측타입별 관측오차를 분석하기 위해 MINQUE(Minimum Norm Quadratic Unbiased Estimators) 방법을 사용하였으며 단기선 GPS측위 모델을 기반으로 하였다. C1, P2, L1, L2 관측타입에 대해 각각 단위분산을 계산하였으며 그 결과 관측오차는 각각 22.3cm, 27.6cm, 2.5cm, 2.2cm로 나타났다. 단위분산 계산에는 대용량의 컴퓨터 메모리와 복잡한 계산이 필요하다. 이러한 단점을 극복하기 위해 epoch별로 단위분산을 추정하였으며 결과를 분석함으로써 epoch별 계산방법의 유효성을 검증하였다. 분석 결과, 계산된 결과값에 차이가 있긴 했으나 그 차이가 비교적 작기 때문에 대부분의 GPS 응용분야에서의 활용에는 문제가 없을 것으로 판단된다.